Towards Railways Remote Driving: Analysis of Video Streaming Latency and Adaptive Rate Control

TL;DR

This paper analyzes video streaming latency in railway remote driving, compares protocols like RTSP and WebRTC, and proposes an adaptive rate control algorithm to ensure low latency and safety.

Contribution

It introduces an analytical method for calculating streaming latency and proposes a novel rate control algorithm for adaptive video streaming in railway remote driving.

Findings

WebRTC outperforms RTSP in latency reduction

The rate control algorithm effectively maintains low latency under varying network conditions

Proposed solutions improve safety and reliability in remote railway operations

Abstract

Remote driving aims to improve transport systems by promoting efficiency, sustainability, and accessibility. In the railway sector, remote driving makes it possible to increase flexibility, as the driver no longer has to be in the cab. However, this brings several challenges, as it has to provide at least the same level of safety obtained when the driver is in the cab. To achieve it, wireless networks and video streaming technologies gain importance as they should provide real-time track visualization and obstacle detection capabilities to the remote driver. Low latency camera capture, onboard media processing devices, and streaming protocols adapted for wireless links are the necessary enablers to be developed and integrated into the railway infrastructure. This paper compares video streaming protocols such as Real-Time Streaming Protocol (RTSP) and Web Real-Time Communication (WebRTC), as they are the main alternatives based on Real-time Transport Protocol (RTP) protocol to enable low latency. As latency is the main performance metric, this paper also provides a solution to calculate the End-to-End video streaming latency analytically. Finally, the paper proposes a rate control algorithm to adapt the video stream depending on the network capacity. The objective is to keep the latency as low as possible while avoiding any visual artifacts. The proposed solutions are tested in different setups and scenarios to prove their effectiveness before the planned field testing.